{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 29G\r\n",
      "drwxr-xr-x  3 root root 4.0K May  7 02:03 .\r\n",
      "drwxr-xr-x 10 root root 4.0K May  7 19:28 ..\r\n",
      "-rw-r--r--  1 root root 6.4G May  7 00:57 dblp-2017.json\r\n",
      "-rw-r--r--  1 root root 8.5G May  7 01:22 dblp-2018.json\r\n",
      "-rw-r--r--  1 root root 9.3G May  7 02:06 dblp-2019.json\r\n",
      "-rw-r--r--  1 root root 4.2G May  7 01:52 dblp-2020.json\r\n",
      "drwxr-xr-x  2 root root 4.0K May  7 01:02 .ipynb_checkpoints\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lha ./data/dblp/article/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import codecs\n",
    "import gc\n",
    "import json\n",
    "import psutil\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_available_memory():\n",
    "    pc_mem = psutil.virtual_memory()\n",
    "    div = 1024 ** 3\n",
    "    pc_mem\n",
    "    print(\"available memory: {:.3f} Gb, usage {:.3}%\".format(pc_mem.available/div, 100 - 100.0 * pc_mem.available/pc_mem.total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = codecs.open(\"./data/dblp/author2articles/data.json\", \"r\")\n",
    "content = f.read()\n",
    "content_json = json.loads(content)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "author2articles = content_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "year2kwinfo = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2245286/2245286 [00:02<00:00, 880875.69it/s] \n"
     ]
    }
   ],
   "source": [
    "with tqdm(author2articles.items(), total=len(author2articles)) as t:\n",
    "    for author, articles in t:\n",
    "        for name, year in articles.items():\n",
    "            if \"data\" in name:\n",
    "                temp = year2kwinfo.get(year, [])\n",
    "                temp.append({})\n",
    "                temp[-1][\"author\"] = author\n",
    "                temp[-1][\"article\"] = name\n",
    "                year2kwinfo[year] = temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available memory: 121.687 Gb, usage 3.35%\n"
     ]
    }
   ],
   "source": [
    "show_available_memory()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "author2count = {}\n",
    "for year, publishes in year2kwinfo.items():\n",
    "    for temp in publishes:\n",
    "        author = temp[\"author\"]\n",
    "        c = author2count.get(author, 0)\n",
    "        c += 1\n",
    "        author2count[author] = c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Philip S. Yu', 71),\n",
       " ('H. Vincent Poor', 68),\n",
       " ('Athanasios V. Vasilakos', 63),\n",
       " ('Licheng Jiao', 57),\n",
       " ('Jie Wu 0001', 53)]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(author2count.items(), key= lambda x:x[1], reverse= True)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2245286/2245286 [00:00<00:00, 3683495.34it/s]\n"
     ]
    }
   ],
   "source": [
    "bigboss2articles = {}\n",
    "with tqdm(author2articles.items(), total=len(author2articles)) as t:\n",
    "    for author, articles in t:\n",
    "        if author == \"Licheng Jiao\":\n",
    "            for name, year in articles.items():\n",
    "                temp = bigboss2articles.get(year, [])\n",
    "                temp.append(name)\n",
    "                bigboss2articles[year] = temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'&#19968;&#31181;&#25913;&#36827;&#30340;&#23616;&#37096;&#27169;&#31946;c-&#22343;&#20540;&#32858;&#31867;&#20998;&#21106;&#31639;&#27861;&#30740;&#31350; (research on improved local fuzzy c-means clustering segmentation algorithm).'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigboss2articles['2015'][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = []\n",
    "y = []\n",
    "for key in sorted(bigboss2articles.keys()):\n",
    "    x.append(int(key))\n",
    "    y.append(len(bigboss2articles[key]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['seaborn-bright',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-darkgrid',\n",
       " 'grayscale',\n",
       " 'Solarize_Light2',\n",
       " 'fivethirtyeight',\n",
       " 'seaborn-muted',\n",
       " 'bmh',\n",
       " '_classic_test',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-white',\n",
       " 'seaborn-paper',\n",
       " 'ggplot',\n",
       " 'seaborn-dark-palette',\n",
       " 'fast',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-colorblind',\n",
       " 'classic',\n",
       " 'seaborn',\n",
       " 'dark_background',\n",
       " 'tableau-colorblind10']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 4500x3600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,12), dpi= 300)\n",
    "plt.style.use(['fivethirtyeight', 'seaborn-paper'])\n",
    "plt.title(\"the number of Licheng Jiao's articles vs year\")\n",
    "plt.plot(x[:-1],y[:-1])\n",
    "plt.ylim(0)\n",
    "plt.xlabel(\"year\")\n",
    "plt.ylabel(\"number\")\n",
    "plt.savefig(\"./images/焦李成.jpg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = codecs.open(\"./data/dblp/bigboss2articles.json\", \"w\")\n",
    "f.write(json.dumps(bigboss2articles, ensure_ascii= False))\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
